Name
Abstract | ||
|---|---|---|
The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3837/tiis.2018.03.015 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Two-stage linear discrimination analysis,Small size sample problem,Selection and compression method,Approximation matrix method,Regularization | Pattern recognition,Computer science,Artificial intelligence,Linear discriminant analysis,Distributed computing | Journal |
Volume | Issue | ISSN |
12 | 3 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yarui Chen | 1 | 42 | 7.60 |
| Xin Tao | 2 | 8 | 3.87 |
| Congcong Xiong | 3 | 10 | 1.87 |
| JuCheng Yang | 4 | 72 | 7.49 |